Talsma, Carl
[Verfasser:in];
Solander, Kurt C.
[Verfasser:in];
Mudunuru, Maruti K.
[Verfasser:in];
Crawford, Brandon
[Verfasser:in];
Powell, Michelle
[Verfasser:in]
Frost Prediction Using Machine Learning and Deep Neural Network Models for Use on Iot Sensors
Beschreibung:
This study describes accurate, computationally efficient models that can be implemented using IoT sensors for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6 hours to 48 hours. Our results show promising accuracy (6-hour prediction RMSE=1.53-1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from Internet of Things (IoT) sensors at a nearby farm. We calculated the feature importance of the random forest models, and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 hours), while other temperature related parameters provide the majority of information for shorter term predictions. Moreover, the associated computational cost to develop these ML models and run-times are low. This makes our ML-models attractive to be deployed on IoT devices towards real-time monitoring of frost events and damage at commercial farming operations